A Simple and Fast Human Activity Recognition System Using Radio Frequency Energy Harvesting
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | UbiComp/ISWC '21 Adjunct - Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 666-671 |
| ISBN (print) | 9781450384612 |
| Publication status | Published - 2021 |
Publication series
| Name | UbiComp/ISWC - Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers |
|---|
Conference
| Title | 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) and the 2021 International Symposium on Wearable Computers (ISWC 2021) (UbiComp/ISWC 2021) |
|---|---|
| Location | Virtual |
| Place | United States |
| Period | 21 - 26 September 2021 |
Link(s)
Abstract
Recognizing indoor activities of an individual provides useful information in smart living, well-being monitoring, and fitness management. In this paper, we propose a simple and fast human activity recognition (HAR) system based on Radio Frequency energy harvesting (RFEH). The intuition is that the harvested voltage signals of different human activities exhibit distinctive patterns. Utilizing the data collected from four smartphones, the RFEH-based HAR system indicates over 91% accuracy of activity recognition across all devices. By combining the lightweight classifiers and making an ensemble classification, an overall accuracy of over 97% is achieved.
Research Area(s)
- Energy Harvesting, Human Activity, Radio Frequency, Recognition, Wireless Network
Citation Format(s)
A Simple and Fast Human Activity Recognition System Using Radio Frequency Energy Harvesting. / Ni, Tao; Chen, Yongliang; Song, Keqi et al.
UbiComp/ISWC '21 Adjunct - Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers. New York: Association for Computing Machinery, Inc, 2021. p. 666-671 (UbiComp/ISWC - Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers).
UbiComp/ISWC '21 Adjunct - Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers. New York: Association for Computing Machinery, Inc, 2021. p. 666-671 (UbiComp/ISWC - Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
